ANCOVA in
Vocabulary taught (Vocabulary taught)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Vocabulary taught (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Vocabulary taught (measured using pre- and post-tests).
Setting Initial Variables
dv = "vocab.teach"
dv.pos = "vocab.teach.pos"
dv.pre = "vocab.teach.pre"
fatores2 <- c("Sexo","Zona","Cor.Raca","Serie","vocab.teach.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
"Parda"="#b97100","Indígena"="#9F262F",
"Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:Sexo"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
"Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
"Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
"Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
"Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
"Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
"Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
"Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
"Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
"Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
"Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)
for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
"TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
"leitura.compreensao")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "sumary")
gdat <- gdat[which(is.na(gdat$Necessidade.Deficiencia) & !is.na(gdat$Stari.Grupo)),]
dat <- gdat
dat$grupo <- factor(dat[["Stari.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
|
vocab.teach.pre |
98 |
4.082 |
4.0 |
1 |
10 |
1.797 |
0.182 |
0.360 |
2.00 |
NO |
0.786 |
0.790 |
| Experimental |
|
|
|
|
|
vocab.teach.pre |
48 |
3.896 |
4.0 |
1 |
7 |
1.949 |
0.281 |
0.566 |
3.25 |
YES |
0.076 |
-1.182 |
|
|
|
|
|
|
vocab.teach.pre |
146 |
4.021 |
4.0 |
1 |
10 |
1.844 |
0.153 |
0.302 |
2.00 |
NO |
0.508 |
0.114 |
| Controle |
|
|
|
|
|
vocab.teach.pos |
98 |
4.378 |
4.0 |
1 |
8 |
1.881 |
0.190 |
0.377 |
2.75 |
YES |
0.309 |
-0.681 |
| Experimental |
|
|
|
|
|
vocab.teach.pos |
48 |
4.417 |
4.0 |
1 |
10 |
2.152 |
0.311 |
0.625 |
3.00 |
YES |
0.306 |
-0.547 |
|
|
|
|
|
|
vocab.teach.pos |
146 |
4.390 |
4.0 |
1 |
10 |
1.967 |
0.163 |
0.322 |
3.00 |
YES |
0.317 |
-0.539 |
| Controle |
F |
|
|
|
|
vocab.teach.pre |
43 |
4.209 |
4.0 |
2 |
7 |
1.245 |
0.190 |
0.383 |
2.00 |
YES |
0.406 |
-0.585 |
| Controle |
M |
|
|
|
|
vocab.teach.pre |
55 |
3.982 |
4.0 |
1 |
10 |
2.139 |
0.288 |
0.578 |
3.00 |
NO |
0.860 |
0.279 |
| Experimental |
F |
|
|
|
|
vocab.teach.pre |
16 |
4.250 |
4.0 |
1 |
7 |
1.915 |
0.479 |
1.020 |
3.00 |
YES |
-0.227 |
-1.155 |
| Experimental |
M |
|
|
|
|
vocab.teach.pre |
32 |
3.719 |
3.5 |
1 |
7 |
1.971 |
0.348 |
0.711 |
3.00 |
YES |
0.234 |
-1.191 |
| Controle |
F |
|
|
|
|
vocab.teach.pos |
43 |
4.488 |
5.0 |
1 |
8 |
1.609 |
0.245 |
0.495 |
1.50 |
YES |
0.138 |
-0.305 |
| Controle |
M |
|
|
|
|
vocab.teach.pos |
55 |
4.291 |
4.0 |
1 |
8 |
2.079 |
0.280 |
0.562 |
3.00 |
YES |
0.407 |
-0.950 |
| Experimental |
F |
|
|
|
|
vocab.teach.pos |
16 |
4.500 |
4.0 |
2 |
8 |
1.826 |
0.456 |
0.973 |
1.50 |
YES |
0.277 |
-0.998 |
| Experimental |
M |
|
|
|
|
vocab.teach.pos |
32 |
4.375 |
4.0 |
1 |
10 |
2.324 |
0.411 |
0.838 |
3.25 |
YES |
0.318 |
-0.668 |
| Controle |
|
Rural |
|
|
|
vocab.teach.pre |
56 |
3.839 |
4.0 |
1 |
8 |
1.581 |
0.211 |
0.424 |
2.00 |
YES |
0.314 |
-0.344 |
| Controle |
|
Urbana |
|
|
|
vocab.teach.pre |
11 |
4.273 |
4.0 |
1 |
10 |
2.412 |
0.727 |
1.620 |
1.50 |
NO |
1.070 |
0.375 |
| Controle |
|
|
|
|
|
vocab.teach.pre |
31 |
4.452 |
4.0 |
2 |
9 |
1.912 |
0.343 |
0.701 |
2.00 |
NO |
0.722 |
0.006 |
| Experimental |
|
Rural |
|
|
|
vocab.teach.pre |
34 |
3.735 |
3.5 |
1 |
7 |
2.035 |
0.349 |
0.710 |
3.75 |
YES |
0.182 |
-1.324 |
| Experimental |
|
Urbana |
|
|
|
vocab.teach.pre |
5 |
3.800 |
4.0 |
3 |
5 |
0.837 |
0.374 |
1.039 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
|
vocab.teach.pre |
9 |
4.556 |
5.0 |
1 |
7 |
2.068 |
0.689 |
1.590 |
2.00 |
YES |
-0.371 |
-1.309 |
| Controle |
|
Rural |
|
|
|
vocab.teach.pos |
56 |
4.411 |
4.0 |
1 |
8 |
1.847 |
0.247 |
0.495 |
2.25 |
YES |
0.386 |
-0.570 |
| Controle |
|
Urbana |
|
|
|
vocab.teach.pos |
11 |
4.000 |
4.0 |
1 |
7 |
1.897 |
0.572 |
1.275 |
3.00 |
YES |
0.000 |
-1.401 |
| Controle |
|
|
|
|
|
vocab.teach.pos |
31 |
4.452 |
4.0 |
1 |
8 |
1.981 |
0.356 |
0.726 |
2.50 |
YES |
0.256 |
-0.989 |
| Experimental |
|
Rural |
|
|
|
vocab.teach.pos |
34 |
4.441 |
4.0 |
1 |
10 |
2.191 |
0.376 |
0.764 |
2.00 |
YES |
0.371 |
-0.368 |
| Experimental |
|
Urbana |
|
|
|
vocab.teach.pos |
5 |
3.600 |
3.0 |
2 |
6 |
1.517 |
0.678 |
1.883 |
1.00 |
NO |
0.537 |
-1.487 |
| Experimental |
|
|
|
|
|
vocab.teach.pos |
9 |
4.778 |
6.0 |
1 |
8 |
2.386 |
0.795 |
1.834 |
3.00 |
YES |
-0.258 |
-1.587 |
| Controle |
|
|
Parda |
|
|
vocab.teach.pre |
45 |
3.867 |
4.0 |
1 |
8 |
1.575 |
0.235 |
0.473 |
2.00 |
YES |
0.283 |
-0.385 |
| Controle |
|
|
Indígena |
|
|
vocab.teach.pre |
3 |
3.333 |
3.0 |
3 |
4 |
0.577 |
0.333 |
1.434 |
0.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Branca |
|
|
vocab.teach.pre |
11 |
4.364 |
5.0 |
2 |
6 |
1.286 |
0.388 |
0.864 |
1.50 |
YES |
-0.366 |
-1.235 |
| Controle |
|
|
Preta |
|
|
vocab.teach.pre |
1 |
5.000 |
5.0 |
5 |
5 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
|
vocab.teach.pre |
38 |
4.289 |
4.0 |
1 |
10 |
2.205 |
0.358 |
0.725 |
2.00 |
NO |
0.864 |
0.085 |
| Experimental |
|
|
Parda |
|
|
vocab.teach.pre |
18 |
3.944 |
4.0 |
1 |
7 |
2.235 |
0.527 |
1.112 |
4.00 |
YES |
-0.024 |
-1.562 |
| Experimental |
|
|
Indígena |
|
|
vocab.teach.pre |
6 |
3.833 |
3.5 |
1 |
7 |
2.041 |
0.833 |
2.142 |
1.75 |
YES |
0.185 |
-1.389 |
| Experimental |
|
|
Branca |
|
|
vocab.teach.pre |
5 |
3.000 |
3.0 |
2 |
4 |
0.707 |
0.316 |
0.878 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
|
vocab.teach.pre |
19 |
4.105 |
4.0 |
1 |
7 |
1.912 |
0.439 |
0.921 |
3.00 |
YES |
-0.142 |
-1.308 |
| Controle |
|
|
Parda |
|
|
vocab.teach.pos |
45 |
4.333 |
4.0 |
2 |
8 |
1.796 |
0.268 |
0.540 |
2.00 |
NO |
0.634 |
-0.518 |
| Controle |
|
|
Indígena |
|
|
vocab.teach.pos |
3 |
4.000 |
4.0 |
3 |
5 |
1.000 |
0.577 |
2.484 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Branca |
|
|
vocab.teach.pos |
11 |
4.545 |
4.0 |
2 |
8 |
1.572 |
0.474 |
1.056 |
1.00 |
NO |
0.555 |
-0.100 |
| Controle |
|
|
Preta |
|
|
vocab.teach.pos |
1 |
3.000 |
3.0 |
3 |
3 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
|
vocab.teach.pos |
38 |
4.447 |
4.5 |
1 |
8 |
2.152 |
0.349 |
0.707 |
3.00 |
YES |
-0.033 |
-1.183 |
| Experimental |
|
|
Parda |
|
|
vocab.teach.pos |
18 |
3.889 |
4.0 |
1 |
8 |
2.166 |
0.511 |
1.077 |
3.00 |
YES |
0.331 |
-1.232 |
| Experimental |
|
|
Indígena |
|
|
vocab.teach.pos |
6 |
4.000 |
4.0 |
2 |
7 |
1.897 |
0.775 |
1.991 |
2.25 |
YES |
0.293 |
-1.534 |
| Experimental |
|
|
Branca |
|
|
vocab.teach.pos |
5 |
4.800 |
3.0 |
3 |
10 |
3.033 |
1.356 |
3.766 |
2.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
|
vocab.teach.pos |
19 |
4.947 |
5.0 |
1 |
8 |
1.985 |
0.455 |
0.957 |
2.00 |
YES |
-0.375 |
-0.567 |
| Controle |
|
|
|
6 ano |
|
vocab.teach.pre |
26 |
3.462 |
3.0 |
1 |
7 |
1.655 |
0.325 |
0.668 |
2.00 |
YES |
0.345 |
-0.814 |
| Controle |
|
|
|
7 ano |
|
vocab.teach.pre |
28 |
4.071 |
4.0 |
3 |
7 |
1.120 |
0.212 |
0.434 |
2.00 |
NO |
0.629 |
-0.504 |
| Controle |
|
|
|
8 ano |
|
vocab.teach.pre |
17 |
3.824 |
3.0 |
1 |
10 |
2.481 |
0.602 |
1.276 |
3.00 |
NO |
1.022 |
0.025 |
| Controle |
|
|
|
9 ano |
|
vocab.teach.pre |
27 |
4.852 |
5.0 |
2 |
9 |
1.812 |
0.349 |
0.717 |
1.50 |
NO |
0.734 |
0.050 |
| Experimental |
|
|
|
6 ano |
|
vocab.teach.pre |
13 |
4.077 |
4.0 |
1 |
6 |
1.706 |
0.473 |
1.031 |
2.00 |
NO |
-0.574 |
-0.955 |
| Experimental |
|
|
|
7 ano |
|
vocab.teach.pre |
13 |
3.308 |
3.0 |
1 |
7 |
2.136 |
0.593 |
1.291 |
2.00 |
NO |
0.624 |
-1.097 |
| Experimental |
|
|
|
8 ano |
|
vocab.teach.pre |
14 |
4.071 |
4.5 |
1 |
7 |
2.235 |
0.597 |
1.290 |
3.75 |
YES |
-0.006 |
-1.651 |
| Experimental |
|
|
|
9 ano |
|
vocab.teach.pre |
8 |
4.250 |
4.0 |
2 |
7 |
1.581 |
0.559 |
1.322 |
0.75 |
YES |
0.403 |
-1.107 |
| Controle |
|
|
|
6 ano |
|
vocab.teach.pos |
26 |
3.615 |
4.0 |
1 |
8 |
1.899 |
0.372 |
0.767 |
2.75 |
NO |
0.637 |
-0.137 |
| Controle |
|
|
|
7 ano |
|
vocab.teach.pos |
28 |
4.429 |
4.0 |
2 |
8 |
1.709 |
0.323 |
0.663 |
2.00 |
NO |
0.676 |
-0.428 |
| Controle |
|
|
|
8 ano |
|
vocab.teach.pos |
17 |
4.176 |
4.0 |
2 |
8 |
1.944 |
0.472 |
1.000 |
2.00 |
YES |
0.488 |
-1.144 |
| Controle |
|
|
|
9 ano |
|
vocab.teach.pos |
27 |
5.185 |
5.0 |
1 |
8 |
1.755 |
0.338 |
0.694 |
2.00 |
YES |
-0.231 |
-0.542 |
| Experimental |
|
|
|
6 ano |
|
vocab.teach.pos |
13 |
5.077 |
5.0 |
3 |
8 |
1.656 |
0.459 |
1.001 |
3.00 |
YES |
0.396 |
-1.399 |
| Experimental |
|
|
|
7 ano |
|
vocab.teach.pos |
13 |
3.615 |
4.0 |
1 |
7 |
1.758 |
0.488 |
1.062 |
3.00 |
YES |
0.036 |
-0.988 |
| Experimental |
|
|
|
8 ano |
|
vocab.teach.pos |
14 |
5.000 |
5.5 |
1 |
10 |
2.602 |
0.695 |
1.502 |
2.75 |
YES |
0.000 |
-0.917 |
| Experimental |
|
|
|
9 ano |
|
vocab.teach.pos |
8 |
3.625 |
2.5 |
2 |
8 |
2.264 |
0.800 |
1.893 |
2.50 |
NO |
0.860 |
-0.971 |
| Controle |
|
|
|
|
1st quintile |
vocab.teach.pre |
18 |
1.778 |
2.0 |
1 |
2 |
0.428 |
0.101 |
0.213 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
2nd quintile |
vocab.teach.pre |
22 |
3.000 |
3.0 |
3 |
3 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
3rd quintile |
vocab.teach.pre |
41 |
4.463 |
4.0 |
4 |
5 |
0.505 |
0.079 |
0.159 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
4th quintile |
vocab.teach.pre |
9 |
6.000 |
6.0 |
6 |
6 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
5th quintile |
vocab.teach.pre |
8 |
8.125 |
8.0 |
7 |
10 |
1.126 |
0.398 |
0.941 |
2.00 |
YES |
0.320 |
-1.574 |
| Experimental |
|
|
|
|
1st quintile |
vocab.teach.pre |
13 |
1.462 |
1.0 |
1 |
2 |
0.519 |
0.144 |
0.314 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.teach.pre |
8 |
3.000 |
3.0 |
3 |
3 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.teach.pre |
15 |
4.400 |
4.0 |
4 |
5 |
0.507 |
0.131 |
0.281 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
4th quintile |
vocab.teach.pre |
6 |
6.000 |
6.0 |
6 |
6 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
5th quintile |
vocab.teach.pre |
6 |
7.000 |
7.0 |
7 |
7 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
1st quintile |
vocab.teach.pos |
18 |
3.278 |
3.0 |
1 |
8 |
1.708 |
0.403 |
0.850 |
2.00 |
NO |
0.928 |
0.806 |
| Controle |
|
|
|
|
2nd quintile |
vocab.teach.pos |
22 |
4.091 |
4.5 |
1 |
8 |
1.630 |
0.348 |
0.723 |
2.00 |
YES |
0.239 |
-0.395 |
| Controle |
|
|
|
|
3rd quintile |
vocab.teach.pos |
41 |
4.488 |
4.0 |
1 |
8 |
1.804 |
0.282 |
0.570 |
3.00 |
YES |
0.343 |
-0.639 |
| Controle |
|
|
|
|
4th quintile |
vocab.teach.pos |
9 |
4.667 |
5.0 |
2 |
7 |
1.732 |
0.577 |
1.331 |
2.00 |
YES |
-0.057 |
-1.453 |
| Controle |
|
|
|
|
5th quintile |
vocab.teach.pos |
8 |
6.750 |
7.0 |
4 |
8 |
1.389 |
0.491 |
1.161 |
2.00 |
NO |
-0.735 |
-0.810 |
| Experimental |
|
|
|
|
1st quintile |
vocab.teach.pos |
13 |
3.692 |
4.0 |
1 |
8 |
1.888 |
0.524 |
1.141 |
2.00 |
YES |
0.479 |
-0.171 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.teach.pos |
8 |
4.625 |
4.0 |
2 |
10 |
2.387 |
0.844 |
1.995 |
1.25 |
NO |
1.216 |
0.427 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.teach.pos |
15 |
4.600 |
5.0 |
1 |
7 |
2.165 |
0.559 |
1.199 |
4.00 |
YES |
-0.265 |
-1.615 |
| Experimental |
|
|
|
|
4th quintile |
vocab.teach.pos |
6 |
5.333 |
5.0 |
2 |
8 |
2.422 |
0.989 |
2.542 |
3.50 |
YES |
-0.042 |
-1.881 |
| Experimental |
|
|
|
|
5th quintile |
vocab.teach.pos |
6 |
4.333 |
5.0 |
1 |
7 |
2.338 |
0.955 |
2.454 |
3.00 |
YES |
-0.333 |
-1.819 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "vocab.teach.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, vocab.teach.pos ~ grupo, covariate = vocab.teach.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "vocab.teach.pos", "grupo", covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## NULL
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 vocab.teach.pre 1 143 20.529 1.23e-05 * 0.126000
## 2 grupo 1 143 0.112 7.38e-01 0.000784
| vocab.teach.pre |
1 |
143 |
20.529 |
0.000 |
* |
0.126 |
| grupo |
1 |
143 |
0.112 |
0.738 |
|
0.001 |
pwc <- emmeans_test(wdat, vocab.teach.pos ~ grupo, covariate = vocab.teach.pre,
p.adjust.method = "bonferroni")
| vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
143 |
-0.335 |
0.738 |
0.738 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
vocab.teach |
pre |
pos |
288 |
-1.084 |
0.279 |
0.279 |
ns |
| Experimental |
time |
vocab.teach |
pre |
pos |
288 |
-1.335 |
0.183 |
0.183 |
ns |
ds <- get.descriptives(wdat, "vocab.teach.pos", "grupo", covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
98 |
4.082 |
0.182 |
4.378 |
0.190 |
4.354 |
0.187 |
3.985 |
4.724 |
| Experimental |
48 |
3.896 |
0.281 |
4.417 |
0.311 |
4.464 |
0.267 |
3.935 |
4.993 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "vocab.teach.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "vocab.teach.pos", "grupo", aov, pwc, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "vocab.teach", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.993 0.736
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 144 1.93 0.167
ANCOVA and
Pairwise for two factors grupo:Sexo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
"vocab.teach.pos", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
pdat[["Sexo"]],
level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Sexo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Sexo), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Sexo"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Sexo"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Sexo"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.teach.pre |
1 |
141 |
20.042 |
0.000 |
* |
0.124 |
| grupo |
1 |
141 |
0.121 |
0.728 |
|
0.001 |
| Sexo |
1 |
141 |
0.029 |
0.864 |
|
0.000 |
| grupo:Sexo |
1 |
141 |
0.075 |
0.785 |
|
0.001 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Sexo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Sexo), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
|
F |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
141 |
0.007 |
0.995 |
0.995 |
ns |
|
M |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
141 |
-0.442 |
0.659 |
0.659 |
ns |
| Controle |
|
vocab.teach.pre*Sexo |
vocab.teach.pos |
F |
M |
141 |
0.293 |
0.770 |
0.770 |
ns |
| Experimental |
|
vocab.teach.pre*Sexo |
vocab.teach.pos |
F |
M |
141 |
-0.132 |
0.895 |
0.895 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Sexo"]],
by=c("grupo","Sexo","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
vocab.teach |
pre |
pos |
284 |
-0.674 |
0.501 |
0.501 |
ns |
| Controle |
M |
time |
vocab.teach |
pre |
pos |
284 |
-0.844 |
0.399 |
0.399 |
ns |
| Experimental |
F |
time |
vocab.teach |
pre |
pos |
284 |
-0.368 |
0.713 |
0.713 |
ns |
| Experimental |
M |
time |
vocab.teach |
pre |
pos |
284 |
-1.367 |
0.173 |
0.173 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Sexo"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
by=c("grupo","Sexo"), suffixes = c("","'"))
}
| Controle |
F |
43 |
4.209 |
0.190 |
4.488 |
0.245 |
4.417 |
0.285 |
3.854 |
4.980 |
| Controle |
M |
55 |
3.982 |
0.288 |
4.291 |
0.280 |
4.306 |
0.251 |
3.809 |
4.802 |
| Experimental |
F |
16 |
4.250 |
0.479 |
4.500 |
0.456 |
4.413 |
0.466 |
3.491 |
5.335 |
| Experimental |
M |
32 |
3.719 |
0.348 |
4.375 |
0.411 |
4.489 |
0.331 |
3.836 |
5.142 |
Plots for ancova
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Sexo"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Sexo"]],
subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Sexo"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Sexo"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Sexo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Sexo", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Sexo"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.994 0.781
if (length(unique(pdat[["Sexo"]])) >= 2)
levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 142 1.92 0.129
ANCOVA and
Pairwise for two factors grupo:Zona
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
"vocab.teach.pos", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
pdat[["Zona"]],
level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Zona)
laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Zona,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Zona), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Zona"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Zona"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Zona, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Zona"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Zona)
laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.teach.pre |
1 |
101 |
11.613 |
0.001 |
* |
0.103 |
| grupo |
1 |
101 |
0.004 |
0.948 |
|
0.000 |
| Zona |
1 |
101 |
1.686 |
0.197 |
|
0.016 |
| grupo:Zona |
1 |
101 |
0.078 |
0.781 |
|
0.001 |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Zona,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Zona), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
|
Rural |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
101 |
-0.164 |
0.870 |
0.870 |
ns |
|
Urbana |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
101 |
0.235 |
0.815 |
0.815 |
ns |
| Controle |
|
vocab.teach.pre*Zona |
vocab.teach.pos |
Rural |
Urbana |
101 |
0.911 |
0.365 |
0.365 |
ns |
| Experimental |
|
vocab.teach.pre*Zona |
vocab.teach.pos |
Rural |
Urbana |
101 |
0.967 |
0.336 |
0.336 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Zona"]],
by=c("grupo","Zona","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
vocab.teach |
pre |
pos |
204 |
-1.603 |
0.111 |
0.111 |
ns |
| Controle |
Urbana |
time |
vocab.teach |
pre |
pos |
204 |
0.339 |
0.735 |
0.735 |
ns |
| Experimental |
Rural |
time |
vocab.teach |
pre |
pos |
204 |
-1.542 |
0.125 |
0.125 |
ns |
| Experimental |
Urbana |
time |
vocab.teach |
pre |
pos |
204 |
0.168 |
0.867 |
0.867 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Zona"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
by=c("grupo","Zona"), suffixes = c("","'"))
}
| Controle |
Rural |
56 |
3.839 |
0.211 |
4.411 |
0.247 |
4.414 |
0.249 |
3.920 |
4.908 |
| Controle |
Urbana |
11 |
4.273 |
0.727 |
4.000 |
0.572 |
3.853 |
0.564 |
2.735 |
4.971 |
| Experimental |
Rural |
34 |
3.735 |
0.349 |
4.441 |
0.376 |
4.481 |
0.320 |
3.846 |
5.115 |
| Experimental |
Urbana |
5 |
3.800 |
0.374 |
3.600 |
0.678 |
3.617 |
0.833 |
1.964 |
5.270 |
Plots for ancova
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Zona"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Zona"]],
subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Zona"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2)
plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Zona"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Zona", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Zona", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Zona"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.990 0.619
if (length(unique(pdat[["Zona"]])) >= 2)
levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 102 1.00 0.395
ANCOVA and
Pairwise for two factors grupo:Cor.Raca
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
"vocab.teach.pos", c("grupo","Cor.Raca"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
pdat[["Cor.Raca"]],
level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Cor.Raca,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Cor.Raca), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Cor.Raca"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Cor.Raca"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.teach.pre |
1 |
74 |
9.737 |
0.003 |
* |
0.116 |
| grupo |
1 |
74 |
0.218 |
0.642 |
|
0.003 |
| Cor.Raca |
1 |
74 |
0.622 |
0.433 |
|
0.008 |
| grupo:Cor.Raca |
1 |
74 |
1.252 |
0.267 |
|
0.017 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Cor.Raca,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Cor.Raca), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
|
Parda |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
74 |
0.926 |
0.358 |
0.358 |
ns |
|
Branca |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
74 |
-0.786 |
0.434 |
0.434 |
ns |
| Controle |
|
vocab.teach.pre*Cor.Raca |
vocab.teach.pos |
Parda |
Branca |
74 |
-0.027 |
0.979 |
0.979 |
ns |
| Experimental |
|
vocab.teach.pre*Cor.Raca |
vocab.teach.pos |
Parda |
Branca |
74 |
-1.368 |
0.175 |
0.175 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Parda |
time |
vocab.teach |
pre |
pos |
150 |
-1.217 |
0.225 |
0.225 |
ns |
| Controle |
Branca |
time |
vocab.teach |
pre |
pos |
150 |
-0.234 |
0.815 |
0.815 |
ns |
| Experimental |
Parda |
time |
vocab.teach |
pre |
pos |
150 |
0.092 |
0.927 |
0.927 |
ns |
| Experimental |
Branca |
time |
vocab.teach |
pre |
pos |
150 |
-1.565 |
0.120 |
0.120 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Cor.Raca"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
| Controle |
Branca |
11 |
4.364 |
0.388 |
4.545 |
0.474 |
4.362 |
0.558 |
3.251 |
5.474 |
| Controle |
Parda |
45 |
3.867 |
0.235 |
4.333 |
0.268 |
4.346 |
0.274 |
3.800 |
4.892 |
| Experimental |
Branca |
5 |
3.000 |
0.316 |
4.800 |
1.356 |
5.154 |
0.830 |
3.499 |
6.808 |
| Experimental |
Parda |
18 |
3.944 |
0.527 |
3.889 |
0.511 |
3.871 |
0.434 |
3.007 |
4.735 |
Plots for ancova
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Cor.Raca"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Cor.Raca"]],
subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Cor.Raca"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.979 0.210
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 75 1.27 0.292
ANCOVA and
Pairwise for two factors grupo:Serie
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
"vocab.teach.pos", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
pdat[["Serie"]],
level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Serie)
laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Serie,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Serie), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Serie"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Serie"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Serie, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Serie"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Serie)
laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.teach.pre |
1 |
137 |
14.638 |
0.000 |
* |
0.097 |
| grupo |
1 |
137 |
0.106 |
0.745 |
|
0.001 |
| Serie |
3 |
137 |
0.412 |
0.744 |
|
0.009 |
| grupo:Serie |
3 |
137 |
3.149 |
0.027 |
* |
0.065 |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Serie,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Serie), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
|
6 ano |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
137 |
-2.030 |
0.044 |
0.044 |
* |
|
7 ano |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
137 |
0.918 |
0.360 |
0.360 |
ns |
|
8 ano |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
137 |
-1.129 |
0.261 |
0.261 |
ns |
|
9 ano |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
137 |
1.855 |
0.066 |
0.066 |
ns |
| Controle |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
6 ano |
7 ano |
137 |
-1.232 |
0.220 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
6 ano |
8 ano |
137 |
-0.779 |
0.437 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
6 ano |
9 ano |
137 |
-2.171 |
0.032 |
0.190 |
ns |
| Controle |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
7 ano |
8 ano |
137 |
0.306 |
0.760 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
7 ano |
9 ano |
137 |
-1.013 |
0.313 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
8 ano |
9 ano |
137 |
-1.180 |
0.240 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
6 ano |
7 ano |
137 |
1.687 |
0.094 |
0.563 |
ns |
| Experimental |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
6 ano |
8 ano |
137 |
0.107 |
0.915 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
6 ano |
9 ano |
137 |
1.842 |
0.068 |
0.406 |
ns |
| Experimental |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
7 ano |
8 ano |
137 |
-1.612 |
0.109 |
0.656 |
ns |
| Experimental |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
7 ano |
9 ano |
137 |
0.361 |
0.718 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*Serie |
vocab.teach.pos |
8 ano |
9 ano |
137 |
1.774 |
0.078 |
0.469 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Serie"]],
by=c("grupo","Serie","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
6 ano |
time |
vocab.teach |
pre |
pos |
276 |
-0.297 |
0.767 |
0.767 |
ns |
| Controle |
7 ano |
time |
vocab.teach |
pre |
pos |
276 |
-0.716 |
0.475 |
0.475 |
ns |
| Controle |
8 ano |
time |
vocab.teach |
pre |
pos |
276 |
-0.551 |
0.582 |
0.582 |
ns |
| Controle |
9 ano |
time |
vocab.teach |
pre |
pos |
276 |
-0.656 |
0.512 |
0.512 |
ns |
| Experimental |
6 ano |
time |
vocab.teach |
pre |
pos |
276 |
-1.366 |
0.173 |
0.173 |
ns |
| Experimental |
7 ano |
time |
vocab.teach |
pre |
pos |
276 |
-0.420 |
0.675 |
0.675 |
ns |
| Experimental |
8 ano |
time |
vocab.teach |
pre |
pos |
276 |
-1.316 |
0.189 |
0.189 |
ns |
| Experimental |
9 ano |
time |
vocab.teach |
pre |
pos |
276 |
0.670 |
0.504 |
0.504 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Serie"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
by=c("grupo","Serie"), suffixes = c("","'"))
}
| Controle |
6 ano |
26 |
3.462 |
0.325 |
3.615 |
0.372 |
3.797 |
0.360 |
3.085 |
4.510 |
| Controle |
7 ano |
28 |
4.071 |
0.212 |
4.429 |
0.323 |
4.412 |
0.344 |
3.731 |
5.093 |
| Controle |
8 ano |
17 |
3.824 |
0.602 |
4.176 |
0.472 |
4.241 |
0.442 |
3.366 |
5.115 |
| Controle |
9 ano |
27 |
4.852 |
0.349 |
5.185 |
0.338 |
4.914 |
0.358 |
4.207 |
5.622 |
| Experimental |
6 ano |
13 |
4.077 |
0.473 |
5.077 |
0.459 |
5.059 |
0.505 |
4.059 |
6.058 |
| Experimental |
7 ano |
13 |
3.308 |
0.593 |
3.615 |
0.488 |
3.848 |
0.509 |
2.841 |
4.854 |
| Experimental |
8 ano |
14 |
4.071 |
0.597 |
5.000 |
0.695 |
4.983 |
0.487 |
4.020 |
5.946 |
| Experimental |
9 ano |
8 |
4.250 |
0.559 |
3.625 |
0.800 |
3.550 |
0.644 |
2.276 |
4.825 |
Plots for ancova
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Serie"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Serie"]],
subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Serie"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2)
plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Serie"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Serie", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Serie", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Serie"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.989 0.339
if (length(unique(pdat[["Serie"]])) >= 2)
levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 138 0.643 0.720
ANCOVA
and Pairwise for two factors
grupo:vocab.teach.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["vocab.teach.quintile"]]),],
"vocab.teach.pos", c("grupo","vocab.teach.quintile"))
pdat = pdat[pdat[["vocab.teach.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["vocab.teach.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["vocab.teach.quintile"]] = factor(
pdat[["vocab.teach.quintile"]],
level[["vocab.teach.quintile"]][level[["vocab.teach.quintile"]] %in% unique(pdat[["vocab.teach.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","vocab.teach.quintile")], pdat[,c("id","grupo","vocab.teach.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile)
laov[["grupo:vocab.teach.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["vocab.teach.quintile"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ vocab.teach.quintile,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, vocab.teach.quintile), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.teach.quintile"]])
pwc <- pwc[,c("grupo","vocab.teach.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.teach.quintile")])]
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","vocab.teach.quintile")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:vocab.teach.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","vocab.teach.quintile"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","vocab.teach.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","vocab.teach.quintile","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","vocab.teach.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:vocab.teach.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","vocab.teach.quintile")], wdat[,c("id","grupo","vocab.teach.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:vocab.teach.quintile"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile)
laov[["grupo:vocab.teach.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:vocab.teach.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.teach.pre |
1 |
135 |
2.991 |
0.086 |
|
0.022 |
| grupo |
1 |
135 |
0.446 |
0.505 |
|
0.003 |
| vocab.teach.quintile |
4 |
135 |
0.593 |
0.668 |
|
0.017 |
| grupo:vocab.teach.quintile |
4 |
135 |
1.037 |
0.391 |
|
0.030 |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["vocab.teach.quintile"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ vocab.teach.quintile,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, vocab.teach.quintile), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.teach.quintile"]])
pwc <- pwc[,c("grupo","vocab.teach.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.teach.quintile")])]
}
|
1st quintile |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
135 |
-0.879 |
0.381 |
0.381 |
ns |
|
2nd quintile |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
135 |
-0.695 |
0.488 |
0.488 |
ns |
|
3rd quintile |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
135 |
-0.267 |
0.790 |
0.790 |
ns |
|
4th quintile |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
135 |
-0.680 |
0.498 |
0.498 |
ns |
|
5th quintile |
vocab.teach.pre*grupo |
vocab.teach.pos |
Controle |
Experimental |
135 |
1.618 |
0.108 |
0.108 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
2nd quintile |
135 |
-0.114 |
0.910 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
3rd quintile |
135 |
0.370 |
0.712 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
4th quintile |
135 |
0.690 |
0.492 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
5th quintile |
135 |
0.138 |
0.891 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
2nd quintile |
3rd quintile |
135 |
0.677 |
0.499 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
2nd quintile |
4th quintile |
135 |
0.957 |
0.340 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
2nd quintile |
5th quintile |
135 |
0.209 |
0.835 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
3rd quintile |
4th quintile |
135 |
0.853 |
0.395 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
3rd quintile |
5th quintile |
135 |
-0.051 |
0.960 |
1.000 |
ns |
| Controle |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
4th quintile |
5th quintile |
135 |
-0.698 |
0.486 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
2nd quintile |
135 |
-0.013 |
0.989 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
3rd quintile |
135 |
0.686 |
0.494 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
4th quintile |
135 |
0.589 |
0.557 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
1st quintile |
5th quintile |
135 |
1.257 |
0.211 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
2nd quintile |
3rd quintile |
135 |
0.909 |
0.365 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
2nd quintile |
4th quintile |
135 |
0.751 |
0.454 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
2nd quintile |
5th quintile |
135 |
1.569 |
0.119 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
3rd quintile |
4th quintile |
135 |
0.211 |
0.833 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
3rd quintile |
5th quintile |
135 |
1.432 |
0.154 |
1.000 |
ns |
| Experimental |
|
vocab.teach.pre*vocab.teach.quintile |
vocab.teach.pos |
4th quintile |
5th quintile |
135 |
1.415 |
0.159 |
1.000 |
ns |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","vocab.teach.quintile")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:vocab.teach.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:vocab.teach.quintile"]],
by=c("grupo","vocab.teach.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
1st quintile |
time |
vocab.teach |
pre |
pos |
272 |
-3.296 |
0.001 |
0.001 |
** |
| Controle |
2nd quintile |
time |
vocab.teach |
pre |
pos |
272 |
-2.650 |
0.009 |
0.009 |
** |
| Controle |
3rd quintile |
time |
vocab.teach |
pre |
pos |
272 |
-0.081 |
0.936 |
0.936 |
ns |
| Controle |
4th quintile |
time |
vocab.teach |
pre |
pos |
272 |
2.071 |
0.039 |
0.039 |
* |
| Controle |
5th quintile |
time |
vocab.teach |
pre |
pos |
272 |
2.014 |
0.045 |
0.045 |
* |
| Experimental |
1st quintile |
time |
vocab.teach |
pre |
pos |
272 |
-4.165 |
0.000 |
0.000 |
**** |
| Experimental |
2nd quintile |
time |
vocab.teach |
pre |
pos |
272 |
-2.380 |
0.018 |
0.018 |
* |
| Experimental |
3rd quintile |
time |
vocab.teach |
pre |
pos |
272 |
-0.401 |
0.689 |
0.689 |
ns |
| Experimental |
4th quintile |
time |
vocab.teach |
pre |
pos |
272 |
0.846 |
0.398 |
0.398 |
ns |
| Experimental |
5th quintile |
time |
vocab.teach |
pre |
pos |
272 |
3.383 |
0.001 |
0.001 |
*** |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","vocab.teach.quintile"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","vocab.teach.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","vocab.teach.quintile","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","vocab.teach.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:vocab.teach.quintile"]] <- merge(ds, lemms[["grupo:vocab.teach.quintile"]],
by=c("grupo","vocab.teach.quintile"), suffixes = c("","'"))
}
| Controle |
1st quintile |
18 |
1.778 |
0.101 |
3.278 |
0.403 |
4.618 |
0.891 |
2.857 |
6.379 |
| Controle |
2nd quintile |
22 |
3.000 |
0.000 |
4.091 |
0.348 |
4.701 |
0.531 |
3.651 |
5.751 |
| Controle |
3rd quintile |
41 |
4.463 |
0.079 |
4.488 |
0.282 |
4.223 |
0.329 |
3.573 |
4.873 |
| Controle |
4th quintile |
9 |
6.000 |
0.000 |
4.667 |
0.577 |
3.484 |
0.924 |
1.657 |
5.310 |
| Controle |
5th quintile |
8 |
8.125 |
0.398 |
6.750 |
0.491 |
4.297 |
1.564 |
1.205 |
7.389 |
| Experimental |
1st quintile |
13 |
1.462 |
0.144 |
3.692 |
0.524 |
5.222 |
1.024 |
3.197 |
7.247 |
| Experimental |
2nd quintile |
8 |
3.000 |
0.000 |
4.625 |
0.844 |
5.235 |
0.747 |
3.758 |
6.712 |
| Experimental |
3rd quintile |
15 |
4.400 |
0.131 |
4.600 |
0.559 |
4.373 |
0.498 |
3.388 |
5.358 |
| Experimental |
4th quintile |
6 |
6.000 |
0.000 |
5.333 |
0.989 |
4.150 |
1.022 |
2.128 |
6.172 |
| Experimental |
5th quintile |
6 |
7.000 |
0.000 |
4.333 |
0.955 |
2.553 |
1.280 |
0.022 |
5.083 |
Plots for ancova
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "vocab.teach.quintile", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["vocab.teach.quintile"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "vocab.teach.quintile", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:vocab.teach.quintile"]],
subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"))
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
plots[["grupo:vocab.teach.quintile"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","vocab.teach.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
plots[["grupo:vocab.teach.quintile"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","vocab.teach.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "vocab.teach.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.teach.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "vocab.teach.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = vocab.teach.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.teach.quintile"))) +
ggplot2::scale_color_manual(values = color[["vocab.teach.quintile"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile, data = wdat))
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.989 0.301
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*vocab.teach.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 9 136 0.350 0.956
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
|
vocab.teach.pre |
98 |
4.082 |
4.0 |
1 |
10 |
1.797 |
0.182 |
0.360 |
2.00 |
NO |
0.786 |
0.790 |
| Experimental |
|
|
|
|
|
vocab.teach.pre |
48 |
3.896 |
4.0 |
1 |
7 |
1.949 |
0.281 |
0.566 |
3.25 |
YES |
0.076 |
-1.182 |
|
|
|
|
|
|
vocab.teach.pre |
146 |
4.021 |
4.0 |
1 |
10 |
1.844 |
0.153 |
0.302 |
2.00 |
NO |
0.508 |
0.114 |
| Controle |
|
|
|
|
|
vocab.teach.pos |
98 |
4.378 |
4.0 |
1 |
8 |
1.881 |
0.190 |
0.377 |
2.75 |
YES |
0.309 |
-0.681 |
| Experimental |
|
|
|
|
|
vocab.teach.pos |
48 |
4.417 |
4.0 |
1 |
10 |
2.152 |
0.311 |
0.625 |
3.00 |
YES |
0.306 |
-0.547 |
|
|
|
|
|
|
vocab.teach.pos |
146 |
4.390 |
4.0 |
1 |
10 |
1.967 |
0.163 |
0.322 |
3.00 |
YES |
0.317 |
-0.539 |
| Controle |
F |
|
|
|
|
vocab.teach.pre |
43 |
4.209 |
4.0 |
2 |
7 |
1.245 |
0.190 |
0.383 |
2.00 |
YES |
0.406 |
-0.585 |
| Controle |
M |
|
|
|
|
vocab.teach.pre |
55 |
3.982 |
4.0 |
1 |
10 |
2.139 |
0.288 |
0.578 |
3.00 |
NO |
0.860 |
0.279 |
| Experimental |
F |
|
|
|
|
vocab.teach.pre |
16 |
4.250 |
4.0 |
1 |
7 |
1.915 |
0.479 |
1.020 |
3.00 |
YES |
-0.227 |
-1.155 |
| Experimental |
M |
|
|
|
|
vocab.teach.pre |
32 |
3.719 |
3.5 |
1 |
7 |
1.971 |
0.348 |
0.711 |
3.00 |
YES |
0.234 |
-1.191 |
| Controle |
F |
|
|
|
|
vocab.teach.pos |
43 |
4.488 |
5.0 |
1 |
8 |
1.609 |
0.245 |
0.495 |
1.50 |
YES |
0.138 |
-0.305 |
| Controle |
M |
|
|
|
|
vocab.teach.pos |
55 |
4.291 |
4.0 |
1 |
8 |
2.079 |
0.280 |
0.562 |
3.00 |
YES |
0.407 |
-0.950 |
| Experimental |
F |
|
|
|
|
vocab.teach.pos |
16 |
4.500 |
4.0 |
2 |
8 |
1.826 |
0.456 |
0.973 |
1.50 |
YES |
0.277 |
-0.998 |
| Experimental |
M |
|
|
|
|
vocab.teach.pos |
32 |
4.375 |
4.0 |
1 |
10 |
2.324 |
0.411 |
0.838 |
3.25 |
YES |
0.318 |
-0.668 |
| Controle |
|
Rural |
|
|
|
vocab.teach.pre |
56 |
3.839 |
4.0 |
1 |
8 |
1.581 |
0.211 |
0.424 |
2.00 |
YES |
0.314 |
-0.344 |
| Controle |
|
Urbana |
|
|
|
vocab.teach.pre |
11 |
4.273 |
4.0 |
1 |
10 |
2.412 |
0.727 |
1.620 |
1.50 |
NO |
1.070 |
0.375 |
| Experimental |
|
Rural |
|
|
|
vocab.teach.pre |
34 |
3.735 |
3.5 |
1 |
7 |
2.035 |
0.349 |
0.710 |
3.75 |
YES |
0.182 |
-1.324 |
| Experimental |
|
Urbana |
|
|
|
vocab.teach.pre |
5 |
3.800 |
4.0 |
3 |
5 |
0.837 |
0.374 |
1.039 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
Rural |
|
|
|
vocab.teach.pos |
56 |
4.411 |
4.0 |
1 |
8 |
1.847 |
0.247 |
0.495 |
2.25 |
YES |
0.386 |
-0.570 |
| Controle |
|
Urbana |
|
|
|
vocab.teach.pos |
11 |
4.000 |
4.0 |
1 |
7 |
1.897 |
0.572 |
1.275 |
3.00 |
YES |
0.000 |
-1.401 |
| Experimental |
|
Rural |
|
|
|
vocab.teach.pos |
34 |
4.441 |
4.0 |
1 |
10 |
2.191 |
0.376 |
0.764 |
2.00 |
YES |
0.371 |
-0.368 |
| Experimental |
|
Urbana |
|
|
|
vocab.teach.pos |
5 |
3.600 |
3.0 |
2 |
6 |
1.517 |
0.678 |
1.883 |
1.00 |
NO |
0.537 |
-1.487 |
| Controle |
|
|
Parda |
|
|
vocab.teach.pre |
45 |
3.867 |
4.0 |
1 |
8 |
1.575 |
0.235 |
0.473 |
2.00 |
YES |
0.283 |
-0.385 |
| Controle |
|
|
Branca |
|
|
vocab.teach.pre |
11 |
4.364 |
5.0 |
2 |
6 |
1.286 |
0.388 |
0.864 |
1.50 |
YES |
-0.366 |
-1.235 |
| Experimental |
|
|
Parda |
|
|
vocab.teach.pre |
18 |
3.944 |
4.0 |
1 |
7 |
2.235 |
0.527 |
1.112 |
4.00 |
YES |
-0.024 |
-1.562 |
| Experimental |
|
|
Branca |
|
|
vocab.teach.pre |
5 |
3.000 |
3.0 |
2 |
4 |
0.707 |
0.316 |
0.878 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Parda |
|
|
vocab.teach.pos |
45 |
4.333 |
4.0 |
2 |
8 |
1.796 |
0.268 |
0.540 |
2.00 |
NO |
0.634 |
-0.518 |
| Controle |
|
|
Branca |
|
|
vocab.teach.pos |
11 |
4.545 |
4.0 |
2 |
8 |
1.572 |
0.474 |
1.056 |
1.00 |
NO |
0.555 |
-0.100 |
| Experimental |
|
|
Parda |
|
|
vocab.teach.pos |
18 |
3.889 |
4.0 |
1 |
8 |
2.166 |
0.511 |
1.077 |
3.00 |
YES |
0.331 |
-1.232 |
| Experimental |
|
|
Branca |
|
|
vocab.teach.pos |
5 |
4.800 |
3.0 |
3 |
10 |
3.033 |
1.356 |
3.766 |
2.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
6 ano |
|
vocab.teach.pre |
26 |
3.462 |
3.0 |
1 |
7 |
1.655 |
0.325 |
0.668 |
2.00 |
YES |
0.345 |
-0.814 |
| Controle |
|
|
|
7 ano |
|
vocab.teach.pre |
28 |
4.071 |
4.0 |
3 |
7 |
1.120 |
0.212 |
0.434 |
2.00 |
NO |
0.629 |
-0.504 |
| Controle |
|
|
|
8 ano |
|
vocab.teach.pre |
17 |
3.824 |
3.0 |
1 |
10 |
2.481 |
0.602 |
1.276 |
3.00 |
NO |
1.022 |
0.025 |
| Controle |
|
|
|
9 ano |
|
vocab.teach.pre |
27 |
4.852 |
5.0 |
2 |
9 |
1.812 |
0.349 |
0.717 |
1.50 |
NO |
0.734 |
0.050 |
| Experimental |
|
|
|
6 ano |
|
vocab.teach.pre |
13 |
4.077 |
4.0 |
1 |
6 |
1.706 |
0.473 |
1.031 |
2.00 |
NO |
-0.574 |
-0.955 |
| Experimental |
|
|
|
7 ano |
|
vocab.teach.pre |
13 |
3.308 |
3.0 |
1 |
7 |
2.136 |
0.593 |
1.291 |
2.00 |
NO |
0.624 |
-1.097 |
| Experimental |
|
|
|
8 ano |
|
vocab.teach.pre |
14 |
4.071 |
4.5 |
1 |
7 |
2.235 |
0.597 |
1.290 |
3.75 |
YES |
-0.006 |
-1.651 |
| Experimental |
|
|
|
9 ano |
|
vocab.teach.pre |
8 |
4.250 |
4.0 |
2 |
7 |
1.581 |
0.559 |
1.322 |
0.75 |
YES |
0.403 |
-1.107 |
| Controle |
|
|
|
6 ano |
|
vocab.teach.pos |
26 |
3.615 |
4.0 |
1 |
8 |
1.899 |
0.372 |
0.767 |
2.75 |
NO |
0.637 |
-0.137 |
| Controle |
|
|
|
7 ano |
|
vocab.teach.pos |
28 |
4.429 |
4.0 |
2 |
8 |
1.709 |
0.323 |
0.663 |
2.00 |
NO |
0.676 |
-0.428 |
| Controle |
|
|
|
8 ano |
|
vocab.teach.pos |
17 |
4.176 |
4.0 |
2 |
8 |
1.944 |
0.472 |
1.000 |
2.00 |
YES |
0.488 |
-1.144 |
| Controle |
|
|
|
9 ano |
|
vocab.teach.pos |
27 |
5.185 |
5.0 |
1 |
8 |
1.755 |
0.338 |
0.694 |
2.00 |
YES |
-0.231 |
-0.542 |
| Experimental |
|
|
|
6 ano |
|
vocab.teach.pos |
13 |
5.077 |
5.0 |
3 |
8 |
1.656 |
0.459 |
1.001 |
3.00 |
YES |
0.396 |
-1.399 |
| Experimental |
|
|
|
7 ano |
|
vocab.teach.pos |
13 |
3.615 |
4.0 |
1 |
7 |
1.758 |
0.488 |
1.062 |
3.00 |
YES |
0.036 |
-0.988 |
| Experimental |
|
|
|
8 ano |
|
vocab.teach.pos |
14 |
5.000 |
5.5 |
1 |
10 |
2.602 |
0.695 |
1.502 |
2.75 |
YES |
0.000 |
-0.917 |
| Experimental |
|
|
|
9 ano |
|
vocab.teach.pos |
8 |
3.625 |
2.5 |
2 |
8 |
2.264 |
0.800 |
1.893 |
2.50 |
NO |
0.860 |
-0.971 |
| Controle |
|
|
|
|
1st quintile |
vocab.teach.pre |
18 |
1.778 |
2.0 |
1 |
2 |
0.428 |
0.101 |
0.213 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
2nd quintile |
vocab.teach.pre |
22 |
3.000 |
3.0 |
3 |
3 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
3rd quintile |
vocab.teach.pre |
41 |
4.463 |
4.0 |
4 |
5 |
0.505 |
0.079 |
0.159 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
4th quintile |
vocab.teach.pre |
9 |
6.000 |
6.0 |
6 |
6 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
5th quintile |
vocab.teach.pre |
8 |
8.125 |
8.0 |
7 |
10 |
1.126 |
0.398 |
0.941 |
2.00 |
YES |
0.320 |
-1.574 |
| Experimental |
|
|
|
|
1st quintile |
vocab.teach.pre |
13 |
1.462 |
1.0 |
1 |
2 |
0.519 |
0.144 |
0.314 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.teach.pre |
8 |
3.000 |
3.0 |
3 |
3 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.teach.pre |
15 |
4.400 |
4.0 |
4 |
5 |
0.507 |
0.131 |
0.281 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
4th quintile |
vocab.teach.pre |
6 |
6.000 |
6.0 |
6 |
6 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
5th quintile |
vocab.teach.pre |
6 |
7.000 |
7.0 |
7 |
7 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
1st quintile |
vocab.teach.pos |
18 |
3.278 |
3.0 |
1 |
8 |
1.708 |
0.403 |
0.850 |
2.00 |
NO |
0.928 |
0.806 |
| Controle |
|
|
|
|
2nd quintile |
vocab.teach.pos |
22 |
4.091 |
4.5 |
1 |
8 |
1.630 |
0.348 |
0.723 |
2.00 |
YES |
0.239 |
-0.395 |
| Controle |
|
|
|
|
3rd quintile |
vocab.teach.pos |
41 |
4.488 |
4.0 |
1 |
8 |
1.804 |
0.282 |
0.570 |
3.00 |
YES |
0.343 |
-0.639 |
| Controle |
|
|
|
|
4th quintile |
vocab.teach.pos |
9 |
4.667 |
5.0 |
2 |
7 |
1.732 |
0.577 |
1.331 |
2.00 |
YES |
-0.057 |
-1.453 |
| Controle |
|
|
|
|
5th quintile |
vocab.teach.pos |
8 |
6.750 |
7.0 |
4 |
8 |
1.389 |
0.491 |
1.161 |
2.00 |
NO |
-0.735 |
-0.810 |
| Experimental |
|
|
|
|
1st quintile |
vocab.teach.pos |
13 |
3.692 |
4.0 |
1 |
8 |
1.888 |
0.524 |
1.141 |
2.00 |
YES |
0.479 |
-0.171 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.teach.pos |
8 |
4.625 |
4.0 |
2 |
10 |
2.387 |
0.844 |
1.995 |
1.25 |
NO |
1.216 |
0.427 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.teach.pos |
15 |
4.600 |
5.0 |
1 |
7 |
2.165 |
0.559 |
1.199 |
4.00 |
YES |
-0.265 |
-1.615 |
| Experimental |
|
|
|
|
4th quintile |
vocab.teach.pos |
6 |
5.333 |
5.0 |
2 |
8 |
2.422 |
0.989 |
2.542 |
3.50 |
YES |
-0.042 |
-1.881 |
| Experimental |
|
|
|
|
5th quintile |
vocab.teach.pos |
6 |
4.333 |
5.0 |
1 |
7 |
2.338 |
0.955 |
2.454 |
3.00 |
YES |
-0.333 |
-1.819 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
143 |
0.112 |
0.738 |
|
0.001 |
1 |
143 |
0.112 |
0.738 |
|
0.001 |
| 2 |
vocab.teach.pre |
1 |
143 |
20.529 |
0.000 |
* |
0.126 |
1 |
143 |
20.529 |
0.000 |
* |
0.126 |
| 4 |
grupo:Sexo |
1 |
141 |
0.075 |
0.785 |
|
0.001 |
1 |
141 |
0.075 |
0.785 |
|
0.001 |
| 5 |
Sexo |
1 |
141 |
0.029 |
0.864 |
|
0.000 |
1 |
141 |
0.029 |
0.864 |
|
0.000 |
| 8 |
grupo:Zona |
1 |
101 |
0.078 |
0.781 |
|
0.001 |
1 |
101 |
0.078 |
0.781 |
|
0.001 |
| 10 |
Zona |
1 |
101 |
1.686 |
0.197 |
|
0.016 |
1 |
101 |
1.686 |
0.197 |
|
0.016 |
| 11 |
Cor.Raca |
1 |
74 |
0.622 |
0.433 |
|
0.008 |
1 |
74 |
0.622 |
0.433 |
|
0.008 |
| 13 |
grupo:Cor.Raca |
1 |
74 |
1.252 |
0.267 |
|
0.017 |
1 |
74 |
1.252 |
0.267 |
|
0.017 |
| 16 |
grupo:Serie |
3 |
137 |
3.149 |
0.027 |
* |
0.065 |
3 |
137 |
3.149 |
0.027 |
* |
0.065 |
| 17 |
Serie |
3 |
137 |
0.412 |
0.744 |
|
0.009 |
3 |
137 |
0.412 |
0.744 |
|
0.009 |
| 20 |
grupo:vocab.teach.quintile |
4 |
135 |
1.037 |
0.391 |
|
0.030 |
4 |
135 |
1.037 |
0.391 |
|
0.030 |
| 22 |
vocab.teach.quintile |
4 |
135 |
0.593 |
0.668 |
|
0.017 |
4 |
135 |
0.593 |
0.668 |
|
0.017 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
|
pre |
pos |
288 |
-1.084 |
0.279 |
0.279 |
ns |
288 |
-1.084 |
0.279 |
0.279 |
ns |
| Experimental |
|
|
|
|
|
pre |
pos |
288 |
-1.335 |
0.183 |
0.183 |
ns |
288 |
-1.335 |
0.183 |
0.183 |
ns |
|
|
|
|
|
|
Controle |
Experimental |
143 |
-0.335 |
0.738 |
0.738 |
ns |
143 |
-0.335 |
0.738 |
0.738 |
ns |
| Controle |
F |
|
|
|
|
pre |
pos |
284 |
-0.674 |
0.501 |
0.501 |
ns |
284 |
-0.674 |
0.501 |
0.501 |
ns |
| Controle |
M |
|
|
|
|
pre |
pos |
284 |
-0.844 |
0.399 |
0.399 |
ns |
284 |
-0.844 |
0.399 |
0.399 |
ns |
| Controle |
|
|
|
|
|
F |
M |
141 |
0.293 |
0.770 |
0.770 |
ns |
141 |
0.293 |
0.770 |
0.770 |
ns |
| Experimental |
F |
|
|
|
|
pre |
pos |
284 |
-0.368 |
0.713 |
0.713 |
ns |
284 |
-0.368 |
0.713 |
0.713 |
ns |
| Experimental |
M |
|
|
|
|
pre |
pos |
284 |
-1.367 |
0.173 |
0.173 |
ns |
284 |
-1.367 |
0.173 |
0.173 |
ns |
| Experimental |
|
|
|
|
|
F |
M |
141 |
-0.132 |
0.895 |
0.895 |
ns |
141 |
-0.132 |
0.895 |
0.895 |
ns |
|
F |
|
|
|
|
Controle |
Experimental |
141 |
0.007 |
0.995 |
0.995 |
ns |
141 |
0.007 |
0.995 |
0.995 |
ns |
|
M |
|
|
|
|
Controle |
Experimental |
141 |
-0.442 |
0.659 |
0.659 |
ns |
141 |
-0.442 |
0.659 |
0.659 |
ns |
| Controle |
|
|
|
|
|
Rural |
Urbana |
101 |
0.911 |
0.365 |
0.365 |
ns |
101 |
0.911 |
0.365 |
0.365 |
ns |
| Controle |
|
Rural |
|
|
|
pre |
pos |
204 |
-1.603 |
0.111 |
0.111 |
ns |
204 |
-1.603 |
0.111 |
0.111 |
ns |
| Controle |
|
Urbana |
|
|
|
pre |
pos |
204 |
0.339 |
0.735 |
0.735 |
ns |
204 |
0.339 |
0.735 |
0.735 |
ns |
| Experimental |
|
|
|
|
|
Rural |
Urbana |
101 |
0.967 |
0.336 |
0.336 |
ns |
101 |
0.967 |
0.336 |
0.336 |
ns |
| Experimental |
|
Rural |
|
|
|
pre |
pos |
204 |
-1.542 |
0.125 |
0.125 |
ns |
204 |
-1.542 |
0.125 |
0.125 |
ns |
| Experimental |
|
Urbana |
|
|
|
pre |
pos |
204 |
0.168 |
0.867 |
0.867 |
ns |
204 |
0.168 |
0.867 |
0.867 |
ns |
|
|
Rural |
|
|
|
Controle |
Experimental |
101 |
-0.164 |
0.870 |
0.870 |
ns |
101 |
-0.164 |
0.870 |
0.870 |
ns |
|
|
Urbana |
|
|
|
Controle |
Experimental |
101 |
0.235 |
0.815 |
0.815 |
ns |
101 |
0.235 |
0.815 |
0.815 |
ns |
| Controle |
|
|
Branca |
|
|
pre |
pos |
150 |
-0.234 |
0.815 |
0.815 |
ns |
150 |
-0.234 |
0.815 |
0.815 |
ns |
| Controle |
|
|
|
|
|
Parda |
Branca |
74 |
-0.027 |
0.979 |
0.979 |
ns |
74 |
-0.027 |
0.979 |
0.979 |
ns |
| Controle |
|
|
Parda |
|
|
pre |
pos |
150 |
-1.217 |
0.225 |
0.225 |
ns |
150 |
-1.217 |
0.225 |
0.225 |
ns |
| Experimental |
|
|
Branca |
|
|
pre |
pos |
150 |
-1.565 |
0.120 |
0.120 |
ns |
150 |
-1.565 |
0.120 |
0.120 |
ns |
| Experimental |
|
|
|
|
|
Parda |
Branca |
74 |
-1.368 |
0.175 |
0.175 |
ns |
74 |
-1.368 |
0.175 |
0.175 |
ns |
| Experimental |
|
|
Parda |
|
|
pre |
pos |
150 |
0.092 |
0.927 |
0.927 |
ns |
150 |
0.092 |
0.927 |
0.927 |
ns |
|
|
|
Branca |
|
|
Controle |
Experimental |
74 |
-0.786 |
0.434 |
0.434 |
ns |
74 |
-0.786 |
0.434 |
0.434 |
ns |
|
|
|
Parda |
|
|
Controle |
Experimental |
74 |
0.926 |
0.358 |
0.358 |
ns |
74 |
0.926 |
0.358 |
0.358 |
ns |
| Controle |
|
|
|
6 ano |
|
pre |
pos |
276 |
-0.297 |
0.767 |
0.767 |
ns |
276 |
-0.297 |
0.767 |
0.767 |
ns |
| Controle |
|
|
|
7 ano |
|
pre |
pos |
276 |
-0.716 |
0.475 |
0.475 |
ns |
276 |
-0.716 |
0.475 |
0.475 |
ns |
| Controle |
|
|
|
8 ano |
|
pre |
pos |
276 |
-0.551 |
0.582 |
0.582 |
ns |
276 |
-0.551 |
0.582 |
0.582 |
ns |
| Controle |
|
|
|
9 ano |
|
pre |
pos |
276 |
-0.656 |
0.512 |
0.512 |
ns |
276 |
-0.656 |
0.512 |
0.512 |
ns |
| Controle |
|
|
|
|
|
6 ano |
7 ano |
137 |
-1.232 |
0.220 |
1.000 |
ns |
137 |
-1.232 |
0.220 |
1.000 |
ns |
| Controle |
|
|
|
|
|
6 ano |
8 ano |
137 |
-0.779 |
0.437 |
1.000 |
ns |
137 |
-0.779 |
0.437 |
1.000 |
ns |
| Controle |
|
|
|
|
|
6 ano |
9 ano |
137 |
-2.171 |
0.032 |
0.190 |
ns |
137 |
-2.171 |
0.032 |
0.190 |
ns |
| Controle |
|
|
|
|
|
7 ano |
8 ano |
137 |
0.306 |
0.760 |
1.000 |
ns |
137 |
0.306 |
0.760 |
1.000 |
ns |
| Controle |
|
|
|
|
|
7 ano |
9 ano |
137 |
-1.013 |
0.313 |
1.000 |
ns |
137 |
-1.013 |
0.313 |
1.000 |
ns |
| Controle |
|
|
|
|
|
8 ano |
9 ano |
137 |
-1.180 |
0.240 |
1.000 |
ns |
137 |
-1.180 |
0.240 |
1.000 |
ns |
| Experimental |
|
|
|
6 ano |
|
pre |
pos |
276 |
-1.366 |
0.173 |
0.173 |
ns |
276 |
-1.366 |
0.173 |
0.173 |
ns |
| Experimental |
|
|
|
7 ano |
|
pre |
pos |
276 |
-0.420 |
0.675 |
0.675 |
ns |
276 |
-0.420 |
0.675 |
0.675 |
ns |
| Experimental |
|
|
|
8 ano |
|
pre |
pos |
276 |
-1.316 |
0.189 |
0.189 |
ns |
276 |
-1.316 |
0.189 |
0.189 |
ns |
| Experimental |
|
|
|
9 ano |
|
pre |
pos |
276 |
0.670 |
0.504 |
0.504 |
ns |
276 |
0.670 |
0.504 |
0.504 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
7 ano |
137 |
1.687 |
0.094 |
0.563 |
ns |
137 |
1.687 |
0.094 |
0.563 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
8 ano |
137 |
0.107 |
0.915 |
1.000 |
ns |
137 |
0.107 |
0.915 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
9 ano |
137 |
1.842 |
0.068 |
0.406 |
ns |
137 |
1.842 |
0.068 |
0.406 |
ns |
| Experimental |
|
|
|
|
|
7 ano |
8 ano |
137 |
-1.612 |
0.109 |
0.656 |
ns |
137 |
-1.612 |
0.109 |
0.656 |
ns |
| Experimental |
|
|
|
|
|
7 ano |
9 ano |
137 |
0.361 |
0.718 |
1.000 |
ns |
137 |
0.361 |
0.718 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
8 ano |
9 ano |
137 |
1.774 |
0.078 |
0.469 |
ns |
137 |
1.774 |
0.078 |
0.469 |
ns |
|
|
|
|
6 ano |
|
Controle |
Experimental |
137 |
-2.030 |
0.044 |
0.044 |
* |
137 |
-2.030 |
0.044 |
0.044 |
* |
|
|
|
|
7 ano |
|
Controle |
Experimental |
137 |
0.918 |
0.360 |
0.360 |
ns |
137 |
0.918 |
0.360 |
0.360 |
ns |
|
|
|
|
8 ano |
|
Controle |
Experimental |
137 |
-1.129 |
0.261 |
0.261 |
ns |
137 |
-1.129 |
0.261 |
0.261 |
ns |
|
|
|
|
9 ano |
|
Controle |
Experimental |
137 |
1.855 |
0.066 |
0.066 |
ns |
137 |
1.855 |
0.066 |
0.066 |
ns |
| Controle |
|
|
|
|
1st quintile |
pre |
pos |
272 |
-3.296 |
0.001 |
0.001 |
** |
272 |
-3.296 |
0.001 |
0.001 |
** |
| Controle |
|
|
|
|
2nd quintile |
pre |
pos |
272 |
-2.650 |
0.009 |
0.009 |
** |
272 |
-2.650 |
0.009 |
0.009 |
** |
| Controle |
|
|
|
|
3rd quintile |
pre |
pos |
272 |
-0.081 |
0.936 |
0.936 |
ns |
272 |
-0.081 |
0.936 |
0.936 |
ns |
| Controle |
|
|
|
|
4th quintile |
pre |
pos |
272 |
2.071 |
0.039 |
0.039 |
* |
272 |
2.071 |
0.039 |
0.039 |
* |
| Controle |
|
|
|
|
5th quintile |
pre |
pos |
272 |
2.014 |
0.045 |
0.045 |
* |
272 |
2.014 |
0.045 |
0.045 |
* |
| Controle |
|
|
|
|
|
1st quintile |
2nd quintile |
135 |
-0.114 |
0.910 |
1.000 |
ns |
135 |
-0.114 |
0.910 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
3rd quintile |
135 |
0.370 |
0.712 |
1.000 |
ns |
135 |
0.370 |
0.712 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
4th quintile |
135 |
0.690 |
0.492 |
1.000 |
ns |
135 |
0.690 |
0.492 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
5th quintile |
135 |
0.138 |
0.891 |
1.000 |
ns |
135 |
0.138 |
0.891 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
3rd quintile |
135 |
0.677 |
0.499 |
1.000 |
ns |
135 |
0.677 |
0.499 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
4th quintile |
135 |
0.957 |
0.340 |
1.000 |
ns |
135 |
0.957 |
0.340 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
5th quintile |
135 |
0.209 |
0.835 |
1.000 |
ns |
135 |
0.209 |
0.835 |
1.000 |
ns |
| Controle |
|
|
|
|
|
3rd quintile |
4th quintile |
135 |
0.853 |
0.395 |
1.000 |
ns |
135 |
0.853 |
0.395 |
1.000 |
ns |
| Controle |
|
|
|
|
|
3rd quintile |
5th quintile |
135 |
-0.051 |
0.960 |
1.000 |
ns |
135 |
-0.051 |
0.960 |
1.000 |
ns |
| Controle |
|
|
|
|
|
4th quintile |
5th quintile |
135 |
-0.698 |
0.486 |
1.000 |
ns |
135 |
-0.698 |
0.486 |
1.000 |
ns |
| Experimental |
|
|
|
|
1st quintile |
pre |
pos |
272 |
-4.165 |
0.000 |
0.000 |
**** |
272 |
-4.165 |
0.000 |
0.000 |
**** |
| Experimental |
|
|
|
|
2nd quintile |
pre |
pos |
272 |
-2.380 |
0.018 |
0.018 |
* |
272 |
-2.380 |
0.018 |
0.018 |
* |
| Experimental |
|
|
|
|
3rd quintile |
pre |
pos |
272 |
-0.401 |
0.689 |
0.689 |
ns |
272 |
-0.401 |
0.689 |
0.689 |
ns |
| Experimental |
|
|
|
|
4th quintile |
pre |
pos |
272 |
0.846 |
0.398 |
0.398 |
ns |
272 |
0.846 |
0.398 |
0.398 |
ns |
| Experimental |
|
|
|
|
5th quintile |
pre |
pos |
272 |
3.383 |
0.001 |
0.001 |
*** |
272 |
3.383 |
0.001 |
0.001 |
*** |
| Experimental |
|
|
|
|
|
1st quintile |
2nd quintile |
135 |
-0.013 |
0.989 |
1.000 |
ns |
135 |
-0.013 |
0.989 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
3rd quintile |
135 |
0.686 |
0.494 |
1.000 |
ns |
135 |
0.686 |
0.494 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
4th quintile |
135 |
0.589 |
0.557 |
1.000 |
ns |
135 |
0.589 |
0.557 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
5th quintile |
135 |
1.257 |
0.211 |
1.000 |
ns |
135 |
1.257 |
0.211 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
3rd quintile |
135 |
0.909 |
0.365 |
1.000 |
ns |
135 |
0.909 |
0.365 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
4th quintile |
135 |
0.751 |
0.454 |
1.000 |
ns |
135 |
0.751 |
0.454 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
5th quintile |
135 |
1.569 |
0.119 |
1.000 |
ns |
135 |
1.569 |
0.119 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
3rd quintile |
4th quintile |
135 |
0.211 |
0.833 |
1.000 |
ns |
135 |
0.211 |
0.833 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
3rd quintile |
5th quintile |
135 |
1.432 |
0.154 |
1.000 |
ns |
135 |
1.432 |
0.154 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
4th quintile |
5th quintile |
135 |
1.415 |
0.159 |
1.000 |
ns |
135 |
1.415 |
0.159 |
1.000 |
ns |
|
|
|
|
|
1st quintile |
Controle |
Experimental |
135 |
-0.879 |
0.381 |
0.381 |
ns |
135 |
-0.879 |
0.381 |
0.381 |
ns |
|
|
|
|
|
2nd quintile |
Controle |
Experimental |
135 |
-0.695 |
0.488 |
0.488 |
ns |
135 |
-0.695 |
0.488 |
0.488 |
ns |
|
|
|
|
|
3rd quintile |
Controle |
Experimental |
135 |
-0.267 |
0.790 |
0.790 |
ns |
135 |
-0.267 |
0.790 |
0.790 |
ns |
|
|
|
|
|
4th quintile |
Controle |
Experimental |
135 |
-0.680 |
0.498 |
0.498 |
ns |
135 |
-0.680 |
0.498 |
0.498 |
ns |
|
|
|
|
|
5th quintile |
Controle |
Experimental |
135 |
1.618 |
0.108 |
0.108 |
ns |
135 |
1.618 |
0.108 |
0.108 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
|
98 |
4.082 |
0.182 |
4.378 |
0.190 |
4.354 |
0.187 |
3.985 |
4.724 |
98 |
4.082 |
0.182 |
4.378 |
0.190 |
4.354 |
0.187 |
3.985 |
4.724 |
0 |
| Experimental |
|
|
|
|
|
48 |
3.896 |
0.281 |
4.417 |
0.311 |
4.464 |
0.267 |
3.935 |
4.993 |
48 |
3.896 |
0.281 |
4.417 |
0.311 |
4.464 |
0.267 |
3.935 |
4.993 |
0 |
| Controle |
F |
|
|
|
|
43 |
4.209 |
0.190 |
4.488 |
0.245 |
4.417 |
0.285 |
3.854 |
4.980 |
43 |
4.209 |
0.190 |
4.488 |
0.245 |
4.417 |
0.285 |
3.854 |
4.980 |
0 |
| Controle |
M |
|
|
|
|
55 |
3.982 |
0.288 |
4.291 |
0.280 |
4.306 |
0.251 |
3.809 |
4.802 |
55 |
3.982 |
0.288 |
4.291 |
0.280 |
4.306 |
0.251 |
3.809 |
4.802 |
0 |
| Experimental |
F |
|
|
|
|
16 |
4.250 |
0.479 |
4.500 |
0.456 |
4.413 |
0.466 |
3.491 |
5.335 |
16 |
4.250 |
0.479 |
4.500 |
0.456 |
4.413 |
0.466 |
3.491 |
5.335 |
0 |
| Experimental |
M |
|
|
|
|
32 |
3.719 |
0.348 |
4.375 |
0.411 |
4.489 |
0.331 |
3.836 |
5.142 |
32 |
3.719 |
0.348 |
4.375 |
0.411 |
4.489 |
0.331 |
3.836 |
5.142 |
0 |
| Controle |
|
Rural |
|
|
|
56 |
3.839 |
0.211 |
4.411 |
0.247 |
4.414 |
0.249 |
3.920 |
4.908 |
56 |
3.839 |
0.211 |
4.411 |
0.247 |
4.414 |
0.249 |
3.920 |
4.908 |
0 |
| Controle |
|
Urbana |
|
|
|
11 |
4.273 |
0.727 |
4.000 |
0.572 |
3.853 |
0.564 |
2.735 |
4.971 |
11 |
4.273 |
0.727 |
4.000 |
0.572 |
3.853 |
0.564 |
2.735 |
4.971 |
0 |
| Experimental |
|
Rural |
|
|
|
34 |
3.735 |
0.349 |
4.441 |
0.376 |
4.481 |
0.320 |
3.846 |
5.115 |
34 |
3.735 |
0.349 |
4.441 |
0.376 |
4.481 |
0.320 |
3.846 |
5.115 |
0 |
| Experimental |
|
Urbana |
|
|
|
5 |
3.800 |
0.374 |
3.600 |
0.678 |
3.617 |
0.833 |
1.964 |
5.270 |
5 |
3.800 |
0.374 |
3.600 |
0.678 |
3.617 |
0.833 |
1.964 |
5.270 |
0 |
| Controle |
|
|
Branca |
|
|
11 |
4.364 |
0.388 |
4.545 |
0.474 |
4.362 |
0.558 |
3.251 |
5.474 |
11 |
4.364 |
0.388 |
4.545 |
0.474 |
4.362 |
0.558 |
3.251 |
5.474 |
0 |
| Controle |
|
|
Parda |
|
|
45 |
3.867 |
0.235 |
4.333 |
0.268 |
4.346 |
0.274 |
3.800 |
4.892 |
45 |
3.867 |
0.235 |
4.333 |
0.268 |
4.346 |
0.274 |
3.800 |
4.892 |
0 |
| Experimental |
|
|
Branca |
|
|
5 |
3.000 |
0.316 |
4.800 |
1.356 |
5.154 |
0.830 |
3.499 |
6.808 |
5 |
3.000 |
0.316 |
4.800 |
1.356 |
5.154 |
0.830 |
3.499 |
6.808 |
0 |
| Experimental |
|
|
Parda |
|
|
18 |
3.944 |
0.527 |
3.889 |
0.511 |
3.871 |
0.434 |
3.007 |
4.735 |
18 |
3.944 |
0.527 |
3.889 |
0.511 |
3.871 |
0.434 |
3.007 |
4.735 |
0 |
| Controle |
|
|
|
6 ano |
|
26 |
3.462 |
0.325 |
3.615 |
0.372 |
3.797 |
0.360 |
3.085 |
4.510 |
26 |
3.462 |
0.325 |
3.615 |
0.372 |
3.797 |
0.360 |
3.085 |
4.510 |
0 |
| Controle |
|
|
|
7 ano |
|
28 |
4.071 |
0.212 |
4.429 |
0.323 |
4.412 |
0.344 |
3.731 |
5.093 |
28 |
4.071 |
0.212 |
4.429 |
0.323 |
4.412 |
0.344 |
3.731 |
5.093 |
0 |
| Controle |
|
|
|
8 ano |
|
17 |
3.824 |
0.602 |
4.176 |
0.472 |
4.241 |
0.442 |
3.366 |
5.115 |
17 |
3.824 |
0.602 |
4.176 |
0.472 |
4.241 |
0.442 |
3.366 |
5.115 |
0 |
| Controle |
|
|
|
9 ano |
|
27 |
4.852 |
0.349 |
5.185 |
0.338 |
4.914 |
0.358 |
4.207 |
5.622 |
27 |
4.852 |
0.349 |
5.185 |
0.338 |
4.914 |
0.358 |
4.207 |
5.622 |
0 |
| Experimental |
|
|
|
6 ano |
|
13 |
4.077 |
0.473 |
5.077 |
0.459 |
5.059 |
0.505 |
4.059 |
6.058 |
13 |
4.077 |
0.473 |
5.077 |
0.459 |
5.059 |
0.505 |
4.059 |
6.058 |
0 |
| Experimental |
|
|
|
7 ano |
|
13 |
3.308 |
0.593 |
3.615 |
0.488 |
3.848 |
0.509 |
2.841 |
4.854 |
13 |
3.308 |
0.593 |
3.615 |
0.488 |
3.848 |
0.509 |
2.841 |
4.854 |
0 |
| Experimental |
|
|
|
8 ano |
|
14 |
4.071 |
0.597 |
5.000 |
0.695 |
4.983 |
0.487 |
4.020 |
5.946 |
14 |
4.071 |
0.597 |
5.000 |
0.695 |
4.983 |
0.487 |
4.020 |
5.946 |
0 |
| Experimental |
|
|
|
9 ano |
|
8 |
4.250 |
0.559 |
3.625 |
0.800 |
3.550 |
0.644 |
2.276 |
4.825 |
8 |
4.250 |
0.559 |
3.625 |
0.800 |
3.550 |
0.644 |
2.276 |
4.825 |
0 |
| Controle |
|
|
|
|
1st quintile |
18 |
1.778 |
0.101 |
3.278 |
0.403 |
4.618 |
0.891 |
2.857 |
6.379 |
18 |
1.778 |
0.101 |
3.278 |
0.403 |
4.618 |
0.891 |
2.857 |
6.379 |
0 |
| Controle |
|
|
|
|
2nd quintile |
22 |
3.000 |
0.000 |
4.091 |
0.348 |
4.701 |
0.531 |
3.651 |
5.751 |
22 |
3.000 |
0.000 |
4.091 |
0.348 |
4.701 |
0.531 |
3.651 |
5.751 |
0 |
| Controle |
|
|
|
|
3rd quintile |
41 |
4.463 |
0.079 |
4.488 |
0.282 |
4.223 |
0.329 |
3.573 |
4.873 |
41 |
4.463 |
0.079 |
4.488 |
0.282 |
4.223 |
0.329 |
3.573 |
4.873 |
0 |
| Controle |
|
|
|
|
4th quintile |
9 |
6.000 |
0.000 |
4.667 |
0.577 |
3.484 |
0.924 |
1.657 |
5.310 |
9 |
6.000 |
0.000 |
4.667 |
0.577 |
3.484 |
0.924 |
1.657 |
5.310 |
0 |
| Controle |
|
|
|
|
5th quintile |
8 |
8.125 |
0.398 |
6.750 |
0.491 |
4.297 |
1.564 |
1.205 |
7.389 |
8 |
8.125 |
0.398 |
6.750 |
0.491 |
4.297 |
1.564 |
1.205 |
7.389 |
0 |
| Experimental |
|
|
|
|
1st quintile |
13 |
1.462 |
0.144 |
3.692 |
0.524 |
5.222 |
1.024 |
3.197 |
7.247 |
13 |
1.462 |
0.144 |
3.692 |
0.524 |
5.222 |
1.024 |
3.197 |
7.247 |
0 |
| Experimental |
|
|
|
|
2nd quintile |
8 |
3.000 |
0.000 |
4.625 |
0.844 |
5.235 |
0.747 |
3.758 |
6.712 |
8 |
3.000 |
0.000 |
4.625 |
0.844 |
5.235 |
0.747 |
3.758 |
6.712 |
0 |
| Experimental |
|
|
|
|
3rd quintile |
15 |
4.400 |
0.131 |
4.600 |
0.559 |
4.373 |
0.498 |
3.388 |
5.358 |
15 |
4.400 |
0.131 |
4.600 |
0.559 |
4.373 |
0.498 |
3.388 |
5.358 |
0 |
| Experimental |
|
|
|
|
4th quintile |
6 |
6.000 |
0.000 |
5.333 |
0.989 |
4.150 |
1.022 |
2.128 |
6.172 |
6 |
6.000 |
0.000 |
5.333 |
0.989 |
4.150 |
1.022 |
2.128 |
6.172 |
0 |
| Experimental |
|
|
|
|
5th quintile |
6 |
7.000 |
0.000 |
4.333 |
0.955 |
2.553 |
1.280 |
0.022 |
5.083 |
6 |
7.000 |
0.000 |
4.333 |
0.955 |
2.553 |
1.280 |
0.022 |
5.083 |
0 |